The present embodiments relate to the field of image quality assessment.
Generally, images (e.g., digital photograph, medical image, CT scan image, etc.) go through many stages of processing that affect image quality before the images are outputted. For example, each stage of image processing may introduce distortion that may reduce quality of final image produced. Images acquired by cameras may exhibit distortions due to optics, sensor noise, colour calibration, exposure control, camera motion, etc. Other sources of distortion may include compression algorithms and bit errors. The type of imaging platforms used for processing an image may also affect the quality of image.
Image quality is a characteristic of an image that relates to perceived image degradation as compared to an ideal or perfect image reference. Typically, quality of image is measured through image quality assessment.
In one of the existing techniques, image quality assessment is performed based on Human difference mean opinion scores (DMOS). In human DMOS based techniques, a number of people are asked to rate an image based on image quality perceived through a naked eye. However, the human DMOS based technique is expensive as well as time consuming. In another technique, the image quality assessment is performed based on peak signal to noise ratio (PSNR) quality measure. In yet another technique, the image quality assessment is performed based on mean square error (MSE) quality measure. However, these techniques use a reference image for comparison. Also, these techniques provide inconsistent results with respect to the human DMOS based technique. In order to overcome the above problems, metrics such as structure similarity (SSIM) index that are cognizant with the human DMOS based technique are developed. In the metrics based technique, quality of an image is assessed with respect to initial uncompressed or distortion free image as the reference image. However, the metrics based technique may not be suitable for comparing image quality of two different image sets. For example, the metrics based technique may use a sophisticated registration technique in order to properly align different images to compute image quality of the two different image sets.
The term “different image sets” refers to two sets of images generated using different imaging platforms. The different imaging platforms may use different compression algorithms, image capturing algorithms, different modalities and so on. For example, a specific type of image produced by computerised tomography (CT) scanners from two different manufacturers are referred to as ‘different image sets’.